wehub-resource-sync
67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
import os
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os.environ['CUDA_VISIBLE_DEVICES'] = '0'
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os.environ['ASCEND_RT_VISIBLE_DEVICES'] = '0'
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kwargs = {
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'per_device_train_batch_size': 2,
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'save_steps': 50,
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'gradient_accumulation_steps': 4,
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'num_train_epochs': 3,
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}
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def test_llm():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='Qwen/Qwen2-7B-Instruct',
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dataset=['AI-ModelScope/alpaca-gpt4-data-zh#1000', 'swift/self-cognition#1000'],
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split_dataset_ratio=0.01,
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packing=True,
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max_length=4096,
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attn_impl='flash_attn',
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logging_steps=1,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
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def test_streaming():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='Qwen/Qwen2-7B-Instruct',
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dataset=['AI-ModelScope/alpaca-gpt4-data-zh#10000'],
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packing=True,
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max_length=4096,
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streaming=True,
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attn_impl='flash_attn',
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max_steps=100,
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dataset_num_proc=1,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
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def test_mllm_streaming():
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from swift import InferArguments, SftArguments, infer_main, sft_main
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result = sft_main(
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SftArguments(
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model='Qwen/Qwen2.5-VL-7B-Instruct',
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dataset=['AI-ModelScope/LaTeX_OCR#20000'],
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packing=True,
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max_length=8192,
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streaming=True,
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attn_impl='flash_attn',
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max_steps=100,
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dataset_num_proc=4,
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**kwargs))
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last_model_checkpoint = result['last_model_checkpoint']
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infer_main(InferArguments(adapters=last_model_checkpoint, load_data_args=True, merge_lora=True))
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if __name__ == '__main__':
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# test_llm()
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# test_streaming()
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test_mllm_streaming()
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